RLGO is a Go program based on reinforcement learning techniques. It combines TD learning and TD search, using a million binary features matching simple patterns of stones. RLGO outperformed traditional (pre-Monte-Carlo) programs in 9x9 Go.

Sylvain Gelly’s MoGo (2007) is a Go program based on Monte-Carlo tree search. It was the world’s first master level 9x9 Computer Go program, and the first program to beat a human professional in even games on 9x9 boards and in handicap games on 19x19 boards.

Real-time strategy games are often plagued by pathfinding problems when large numbers of units move around the map. Cooperative pathfinding allows multiple units to coordinate their routes effectively in both space and time.